Abstract
The agricultural sector has an important role in the Indonesian economy. Agriculture provides a national food stocks, especially rice as a staple food of Indonesian people. Weather conditions, especially rainfall, severely affected when the right time to start planting. This is very important because this will affect the productivity of farmers. Therefore, rainfall forecasting system is required to create a calendar season, especially rice plant. In this paper, we propose a rainfall forecasting system based on Fuzzy which is optimized using Genetic Algorithms. Data preprocessing is handled by using Local Regression Smoothing for handling of fluctuating data. This paper implements the Local Regression Smoothing on Evolving Fuzzy algorithm with monthly rainfall data. Based on the accuracy of more than 80%, the result of next months rainfall forecasting could be used in the making of rice plant planting calendar in the Bandung regency with 3 periods of planting season, which are from November to February, from December to March, and from January to April given that a control of water needs in surplus of rainfall, and added water needs in rainfall deficiency.
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Acknowledgments
Authors would like to thank Telkom University for support of this research. Also, the author would like to express a great appreciation to BMKG of Bandung Regency, for the data used in this study and kindly discussion.
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Saputro, A.A.R., Nhita, F., Adiwijaya (2017). An Implementation of Local Regression Smoothing on Evolving Fuzzy Algorithm for Planting Calendar Forecasting Based on Rainfall. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_16
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DOI: https://doi.org/10.1007/978-3-319-51281-5_16
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